Adaptive fusion measurement method and device for long-term deformation of surface of coal mining subsidence area by MT-InSAR

Abstract

The application relates to a long-term deformation MT-InSAR adaptive fusion measurement method and device for a coal mining subsidence area surface, wherein the method comprises the following steps: obtaining surface deformation monitoring data by using multiple modes respectively, and performing at least one of time-space alignment, noise removal and error correction on the surface deformation monitoring data to obtain surface deformation data; the coherence index of the surface deformation data is calculated; a bimodal Gaussian function model constructed based on the deformation law of the coal mining subsidence area is used to generate a spatial adjustment coefficient; an adaptive weighting method is constructed based on the coherence index of the surface deformation data and the spatial adjustment coefficient to calculate the weight of multi-source InSAR data; and the surface deformation monitoring data is weighted and fused by using the weight of the multi-source InSAR data to obtain the final surface deformation result. Therefore, the problem that a single InSAR technology cannot comprehensively reflect the time-space evolution characteristics of long-term deformation of a wide-area coal mining subsidence area surface, resulting in insufficient monitoring accuracy, incomplete coverage, poor adaptability and the like is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geological disaster monitoring, and in particular to a coal mining subsidence area surface long-term deformation MT-InSAR (Multi-Temporal-Interferometric Synthetic Aperture Radar) adaptive fusion measurement method and device. BACKGROUND

[0002] In the related art, with the acceleration of global urbanization, surface subsidence in coal mining subsidence areas has become an important hidden danger threatening ecological security and social development. InSAR (Interferometric Synthetic Aperture Radar) technology has been widely used in surface deformation monitoring due to its advantages of high precision, large range, and all-weather, and further developed MT-InSAR (Multi-Temporal-Interferometric Synthetic Aperture Radar) technologies such as D-InSAR (Differential-Interferometric Synthetic Aperture Radar), SBAS-InSAR (Small Baseline Subset-Interferometric Synthetic Aperture Radar), and DS-InSAR (Distributed Scatterers-Interferometric Synthetic Aperture Radar).

[0003] However, in the related art, D-InSAR technology is susceptible to atmospheric delay, spatial and temporal decorrelation, and other problems, which may lead to inaccurate monitoring results in complex surface environments (such as vegetation-covered areas and coal mining subsidence areas). Although SBAS-InSAR and DS-InSAR technologies perform well in low-coherence areas and long-period deformation monitoring, they may underestimate deformation or have data missing in areas with large deformation. In summary, a single InSAR technology cannot fully reflect the spatio-temporal evolution characteristics of long-term deformation in wide-area coal mining subsidence areas, and there are problems such as insufficient monitoring accuracy, incomplete coverage, and poor adaptability, which need to be solved. SUMMARY

[0004] The application provides a long-term deformation MT-InSAR adaptive fusion measurement method and device for a coal mining subsidence area, to solve the problem that a single InSAR technology cannot comprehensively reflect the space-time evolution characteristics of long-term deformation of a wide-area coal mining subsidence area in the related art, resulting in insufficient monitoring accuracy, incomplete coverage, poor adaptability, and the like.

[0005] The first aspect of the application provides a long-term deformation MT-InSAR adaptive fusion measurement method for a coal mining subsidence area, comprising the following steps: obtaining ground deformation monitoring data in multiple ways respectively, and performing at least one of space-time alignment, noise removal, and error correction on the ground deformation monitoring data to obtain ground deformation data; calculating a coherence index of the ground deformation data; generating a bimodal Gaussian function model based on the deformation law of the coal mining subsidence area to generate a spatial adjustment coefficient; constructing an adaptive weighting method based on the coherence index of the ground deformation data and the spatial adjustment coefficient to calculate the weight of multi-source InSAR data; and performing weighted fusion on the ground deformation monitoring data using the weight of the multi-source InSAR data to obtain the final ground deformation result.

[0006] Through the above technical means, the ground deformation monitoring data is weighted and fused using the weight of the multi-source InSAR data, and then the final ground deformation result is obtained, which can fully utilize the respective advantages of time density, spatial coverage, and coherence stability, utilize the complementary characteristics between multi-source data, realize high-precision monitoring of complex geological environments and long-period ground deformation processes, significantly alleviate the technical bottlenecks of existing single InSAR technology in low-coherence areas, large-amplitude deformation areas, and complex surface environments, such as data failure, misjudgment, or underestimation of deformation, improve the accuracy, stability, and adaptability of the monitoring system, and further provide an efficient, practical, and scalable solution for deformation monitoring in complex geological backgrounds.

[0007] Optionally, in an embodiment of the application, the ground deformation result is verified to obtain an analysis error, and the model parameters of the bimodal Gaussian function model and / or the adaptive weighting method are optimized using the analysis error.

[0008] Through the above technical means, the model parameters of the bimodal Gaussian function model and / or the adaptive weighting method are optimized using the analysis error, which can dynamically adjust the center position, peak weight, or width parameters of the Gaussian function based on the error size, re-distribute the fusion weights of different data sources, significantly improve the model fitting accuracy and spatial consistency of deformation recognition, enhance the generalization ability of the model under different geological backgrounds and deformation characteristics, and thus further improve the overall stability and accuracy of ground deformation monitoring.

[0009] Optionally, in an embodiment of the present application, the multiple modes include multiple modes among D-InSAR mode, SBAS-InSAR mode and DS-InSAR mode.

[0010] Through the above technical means, the surface deformation monitoring data is acquired by using D-InSAR, SBAS-InSAR and DS-InSAR, the advantages of each method in spatial coverage, time resolution and deformation accuracy can be comprehensively utilized, and thus the recognition ability of the surface deformation process is improved.

[0011] Optionally, in an embodiment of the present application, the calculation formula of the weight is:

[0012] ω i (x,y)=γ i (x,y)·α i (x,y),

[0013] wherein, ω i (x,y) represents the weight of the i-th InSAR data at the pixel point (x, y); γ i (x,y) represents the coherence of the data source at (x, y); α i (x,y) represents the adjustment coefficient of the space correlation; x and y represent the coordinates of any pixel point of the InSAR data; the subscript i represents different InSAR data.

[0014] Through the above technical means, the fusion weight of each data source in different regions is determined by combining the coherence index and the spatial adjustment coefficient of the multi-source InSAR data, the region adaptive distribution of the fusion weight is realized, more high-quality data is retained in the high-coherence region, and the data with more stable spatial structure is relied on in the low-coherence or severe deformation region, so that the accuracy and continuity of the surface deformation information after fusion are improved.

[0015] Optionally, in an embodiment of the present application, the calculation of the coherence index of the surface deformation data comprises: acquiring a complex signal of a pixel point; and calculating the coherence index of the surface deformation data according to the complex signal.

[0016] Through the above technical means, the coherence index of the surface deformation data is calculated according to the complex signal, the quality screening and weighting processing of the surface deformation data are performed, and the accuracy and robustness of the subsequent deformation monitoring and data fusion are improved, which has obvious practical value, especially in the low-coherence region such as vegetation coverage, severe terrain change or building shielding.

[0017] Optionally, in an embodiment of the present application, the calculation formula of the double-peak Gaussian function model is:

[0018]

[0019] wherein, a i (x, y) represents a spatial correlation adjustment coefficient; λ represents a peak value; a1 and a2 both represent a main section distribution; b1 and b2 both represent a vertical section distribution, and (x1, y1) and (x2, y2) represent a center position.

[0020] Through the above technical means, the main section distribution, the vertical section distribution and the deformation center position of the deformation area are analyzed, and then a three-dimensional deformation profile model is constructed, which can effectively depict the "W" type or asymmetric "U" type deformation characteristics, improve the adaptability and fitting precision of the model in the actual engineering scene, and is especially suitable for the settlement mode caused by double working faces or non-uniform goaf structure.

[0021] Optionally, in an embodiment of the present application, the fusion formula of the ground surface deformation monitoring data is:

[0022]

[0023] wherein, D Fusion represents the result after fusion; ω i (x, y) represents the weight of the i-th InSAR data at the pixel point (x, y); D i (x, y) represents the ground surface deformation monitoring result of different InSAR data at the spatial position (x, y); and n represents the number of InSAR methods participating in fusion.

[0024] Through the above technical means, the ground surface deformation monitoring results of different InSAR data are utilized, and the confidence weight of each data source in the target area is combined, and then weighted fusion is performed, which can fully exert the complementary advantages of multi-source data in spatial coverage range, time resolution and coherence, realize high-precision, wide-range and long-time continuous monitoring of ground surface deformation information, and can effectively overcome the problems of data failure or deformation underestimation of single InSAR technology in low coherence area, large deformation area or complex ground surface environment, thereby improving the precision, stability and adaptability of the monitoring result.

[0025] The second aspect embodiment of the application provides a long-term deformation MT-InSAR adaptive fusion measurement device for a coal mining subsidence area, comprising: an acquisition module, configured to acquire ground surface deformation monitoring data in multiple ways respectively, and to perform at least one of time-space alignment, noise removal and error correction on the ground surface deformation monitoring data to obtain ground surface deformation data; a calculation module, configured to calculate a coherence index of the ground surface deformation data; a generation module, configured to generate a bimodal Gaussian function model based on the deformation law of the coal mining subsidence area to generate a spatial adjustment coefficient; a construction module, configured to construct an adaptive weighting method based on the coherence index of the ground surface deformation data and the spatial adjustment coefficient to calculate the weight of multi-source InSAR data; and a measurement module, configured to perform weighted fusion on the ground surface deformation monitoring data by using the weight of the multi-source InSAR data to obtain a final ground surface deformation result.

[0026] By means of the above technical means, the ground surface deformation monitoring data is weighted and fused by using the weight of the multi-source InSAR data, and then the final ground surface deformation result is obtained, which can fully exert the respective advantages of time density, spatial coverage and coherence stability, utilize the complementary characteristics between multi-source data, realize high-precision monitoring of complex geological environment and long-period ground surface deformation process, can significantly alleviate the technical bottleneck of existing single InSAR technology in low-coherence areas, large-amplitude deformation areas and complex surface environment, such as data failure, misjudgment or underestimation of deformation, improve the precision, stability and adaptability of the monitoring system, and further provide an efficient, practical and expandable solution for deformation monitoring in complex geological background.

[0027] Optionally, in an embodiment of the application, there is further provided a verification module configured to verify the ground surface deformation result to obtain an analysis error, and an optimization module configured to optimize the model parameters of the bimodal Gaussian function model and / or the adaptive weighting method by using the analysis error.

[0028] By means of the above technical means, the model parameters of the bimodal Gaussian function model and / or the adaptive weighting method are optimized by using the analysis error, the center position, peak weight or width parameter of the Gaussian function can be dynamically adjusted based on the error size, the fusion weight of different data sources is redistributed, the model fitting precision and spatial consistency of deformation recognition can be significantly improved, the generalization ability of the model under different geological backgrounds and deformation characteristics is enhanced, and thus the overall stability and accuracy of ground surface deformation monitoring are further improved.

[0029] Optionally, in an embodiment of the application, the acquisition methods include multiple methods selected from the group consisting of D-InSAR, SBAS-InSAR and DS-InSAR.

[0030] Through the above technical means, the D-InSAR, the SBAS-InSAR and the DS-InSAR are used to obtain the surface deformation monitoring data, the advantages of each method in the spatial coverage, the time resolution and the deformation accuracy can be comprehensively utilized, and therefore the identification capability for the surface deformation process can be improved.

[0031] Optionally, in an embodiment of the present application, the calculation formula of the weight is:

[0032] ω i (x,y)=γ i (x,y)·α i (x,y),

[0033] wherein ω i (x,y) represents the weight of the i-th InSAR data at the pixel point (x, y); γ i (x,y) represents the coherence of the data source at (x, y); α i (x,y) represents the spatial correlation adjustment coefficient; x and y represent the coordinates of any pixel point of the InSAR data; and the subscript i represents different InSAR data.

[0034] Through the above technical means, the coherence index and the spatial adjustment coefficient of the multi-source InSAR data are combined, the weighted function is constructed to determine the fusion weight of each data source in different regions, the region adaptive distribution of the fusion weight can be realized, more high-quality data can be retained in the high-coherence region, and the data with more stable spatial structure is relied on in the low-coherence or severe deformation region, so that the accuracy and continuity of the surface deformation information after fusion can be improved.

[0035] Optionally, in an embodiment of the present application, the calculation module comprises: an acquisition unit, configured to acquire the complex signal of the pixel point; and a calculation unit, configured to calculate the coherence index of the surface deformation data according to the complex signal.

[0036] Through the above technical means, the coherence index of the surface deformation data is calculated according to the complex signal, the quality screening and the weighted processing of the surface deformation data can be performed, the accuracy and the robustness of the subsequent deformation monitoring and data fusion can be improved, and obvious practical value is obtained in the low-coherence region such as the vegetation coverage, the severe terrain change or the building shielding.

[0037] Optionally, in an embodiment of the present application, the calculation formula of the double-peak Gaussian function model is:

[0038]

[0039] wherein α i(x, y) represents a spatial correlation adjustment coefficient; λ represents a peak value; a1 and a2 both represent a main section distribution; b1 and b2 both represent a vertical section distribution, and (x1, y1) and (x2, y2) represent center positions.

[0040] By the above technical means, the main section distribution, the vertical section distribution and the deformation center position of the deformation area are analyzed, and then a three-dimensional deformation profile model is constructed, which can effectively depict the "W" type or asymmetric "U" type deformation characteristics, improve the adaptability and fitting precision of the model in actual engineering scenarios, and is particularly suitable for the settlement mode caused by double working faces or non-uniform goaf structures.

[0041] Optionally, in an embodiment of the present application, the fusion formula of the ground surface deformation monitoring data is:

[0042]

[0043] wherein, D Fusiin represents the result after fusion; ω i (x, y) represents the weight of the i-th InSAR data at the pixel point (x, y); D i (x, y) represents the ground surface deformation monitoring result of different InSAR data at the spatial position (x, y); and n represents the number of InSAR methods participating in fusion.

[0044] By the above technical means, the ground surface deformation monitoring results of different InSAR data are used, and the confidence weight of each data source in the target area is combined, and then weighted fusion is performed, which can fully play the complementary advantages of multi-source data in spatial coverage, time resolution and coherence, realize high-precision, wide-range and long-time continuous monitoring of ground surface deformation information, and effectively overcome the problems of data failure or deformation underestimation of single InSAR technology in low coherence area, large deformation area or complex ground surface environment, thereby improving the precision, stability and adaptability of the monitoring result.

[0045] The third aspect embodiment of the present application provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the program to realize the coal mining subsidence area ground surface long-term deformation MT-InSAR adaptive fusion measurement method as described in the above embodiments.

[0046] The fourth aspect embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize the coal mining subsidence area ground surface long-term deformation MT-InSAR adaptive fusion measurement method as described above.

[0047] Additional aspects and advantages of the application will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and the attached drawings. BRIEF DESCRIPTION OF DRAWINGS

[0048] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, including the appended drawings.

[0049] Figure 1 A schematic diagram of a system architecture of a long-term deformation MT-InSAR adaptive fusion measurement method for a coal mining subsidence area according to an embodiment of the present application;

[0050] Figure 2 A flowchart of a long-term deformation MT-InSAR adaptive fusion measurement method for a coal mining subsidence area according to an embodiment of the present application;

[0051] Figure 3 A schematic diagram of surface monitoring results of three InSAR technologies according to an embodiment of the present application;

[0052] Figure 4 A schematic diagram of InSAR technology coherence results according to an embodiment of the present application;

[0053] Figure 5 A schematic diagram of spatial adjustment coefficient results based on D-InSAR calculation according to an embodiment of the present application;

[0054] Figure 6 A schematic diagram of spatial adjustment coefficient results based on SBAS-InSAR and DS-InSAR calculation according to an embodiment of the present application;

[0055] Figure 7 A schematic diagram of MT-InSAR adaptive fusion data results according to an embodiment of the present application;

[0056] Figure 8 A schematic diagram of single InSAR technology and fusion data main section and vertical section comparison results according to an embodiment of the present application;

[0057] Figure 9 A block schematic diagram of a long-term deformation MT-InSAR adaptive fusion measurement device for a coal mining subsidence area according to an embodiment of the present application;

[0058] Figure 10 A schematic diagram of a structure of an electronic device according to an embodiment of the present application.

[0059] REFERENCE NUMERALS:

[0060] 10 - MT-InSAR adaptive fusion measuring device for long-term deformation of surface in coal mining subsidence area; 100 - acquisition module, 200 - calculation module, 300 - generation module, 400 - construction module, 500 - measurement module; 1001 - memory, 1002 - processor, 1003 - communication interface. DETAILED DESCRIPTION

[0061] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0062] The MT-InSAR adaptive fusion measuring method and device for long-term deformation of surface in coal mining subsidence area of the embodiments of the present application are described below with reference to the accompanying drawings. In view of the technical problems of single InSAR technology being difficult to comprehensively reflect the spatio-temporal evolution characteristics of long-term deformation of surface in wide-area coal mining subsidence area, and insufficient monitoring accuracy, incomplete coverage, and poor adaptability, the present application provides an MT-InSAR adaptive fusion measuring method for long-term deformation of surface in coal mining subsidence area, in which D-InSAR, SBAS-InSAR, and DS-InSAR three kinds of InSAR measurement data are coupled, the coherence index is calculated, and a double-peak Gaussian function model is constructed, and then an adaptive fusion strategy is used for dynamic weighted integration of multi-source InSAR data, which can fully play the complementary advantages of multi-source InSAR data, realize continuous and high-precision monitoring of the surface deformation process, significantly improve the monitoring accuracy and spatial coverage, effectively cope with the limitations of different interference modes in spatial scale and time density, and is especially suitable for deformation identification and evaluation in complex topographic conditions such as coal mining subsidence area and geological disaster-prone area, which can provide an efficient and practical technical means for geological disaster warning and safety decision-making. Thus, the technical problems of single InSAR technology being difficult to comprehensively reflect the spatio-temporal evolution characteristics of long-term deformation of surface in wide-area coal mining subsidence area, resulting in insufficient monitoring accuracy, incomplete coverage, and poor adaptability are solved.

[0063] Before the MT-InSAR adaptive fusion measuring method for long-term deformation of surface in coal mining subsidence area proposed in the embodiments of the present application is described, the application scenarios and system architecture involved in the embodiments of the present application are described.

[0064] Embodiments of the present application take the mined-out subsidence area of Nangou Coal Mine in Xinzhou City of Shanxi Province as the research object, which contains three mined-out working faces 20201, 20202 and 50501. There is a potential influence of the mined-out subsidence area on the ground surface deformation in the research area. In order to improve the accuracy of deformation monitoring and solve the problem of data failure or low deformation estimation in low coherence area and large deformation area by single technology, the embodiments of the present application propose a method of adaptive fusion of bimodal Gaussian function model and MT-InSAR observation data to monitor the ground deformation, and the system architecture can be as shown in Figure 1 The three InSAR measurement data of D-InSAR, SBAS-InSAR and DS-InSAR are coupled, and the complementary characteristics of multi-source data are fully utilized, so as to realize high-precision monitoring of complex geological environment and long-term ground deformation.

[0065] Specifically, Figure 2 A flowchart of a long-term deformation MT-InSAR adaptive fusion measurement method for a coal mining subsidence area provided by the embodiments of the present application.

[0066] As shown in Figure 2 The long-term deformation MT-InSAR adaptive fusion measurement method for the coal mining subsidence area includes the following steps:

[0067] In step S201, the ground deformation monitoring data is obtained by using multiple ways respectively, and at least one of the time and space alignment, noise removal and error correction of the ground deformation monitoring data is performed to obtain the ground deformation data.

[0068] The ground deformation monitoring data refers to the data for reflecting the spatial changes such as displacement, subsidence, uplift or deformation of the ground surface within a certain time range, which can include but is not limited to InSAR phase data, deformation time series, GNSS (Global Navigation Satellite System) coordinate changes, leveling data and structural deformation data, etc.

[0069] Optionally, in an embodiment of the present application, the multiple ways can include but are not limited to multiple ways of D-InSAR, SBAS-InSAR and DS-InSAR.

[0070] It should be noted that the embodiments of the present application can perform time and space alignment on the ground deformation monitoring data obtained by D-InSAR, SBAS-InSAR and DS-InSAR technology, which can ensure that different technologies monitor the ground deformation under the same time reference and spatial resolution; the data is subjected to noise removal, including but not limited to removal of atmospheric delay, orbit error and the influence of noise on deformation monitoring; and an error correction model is used to correct the systematic deviation of each data source, so as to improve the monitoring accuracy.

[0071] As a possible implementation manner, the embodiment of the present application can obtain SLC (Single-look Complex) of Sentinel-1A satellite once every 12 days, and generate registered SAR (Synthetic Aperture Radar) time series data set after basic preprocessing such as data cropping, geocoding, image registration, slant range correction and addition of precise orbit data.

[0072] Specifically, the embodiment of the present application selects Sentinel-1A image data covering the research area and having a time span from August 27, 2023 to May 5, 2024, the imaging mode of which can be IW (Interferometric Wide Swath Mode), and the polarization mode (indicating the direction of electromagnetic wave vibration of radar wave) can be VV (Dual Polarization). After screening, the SAR image data of the research area can have the details as shown in Table 1. Table 1 is a detailed information table of Sentinel-1A satellite data.

[0073] Table 1

[0074] Serial number Imaging date Imaging mode Polarization mode Data type 1 20230827 IW VV SLC 2 20230908 IW VV SLC 3 20230920 IW VV SLC 4 20231002 IW VV SLC 5 20231014 IW VV SLC 6 20231026 IW VV SLC 7 20231107 IW VV SLC 8 20231119 IW VV SLC 9 20231201 IW VV SLC 10 20231213 IW VV SLC 11 20231225 IW VV SLC 12 20240106 IW VV SLC 13 20240118 IW VV SLC 14 20240130 IW VV SLC 15 20240211 IW VV SLC 16 20240223 IW VV SLC 17 20240306 IW VV SLC 18 20240318 IW VV SLC 19 20240330 Figure 3 Figure 4 Figure 5 20 20240411 Figure 6 Figure 5 Figure 6 21 20240423 Figure 7 Figure 8 Figure 8 22 20240505 Figure 9 Figure 9 Figure 10

[0075] The embodiment of the present application can update the orbit state vector of the SAR image by applying the high-precision satellite position and speed information provided in the precise orbit file, which can effectively reduce the deviation problem caused by satellite orbit error. At the same time, the introduction of precise orbit data can significantly improve the orbit accuracy of the SAR image, thereby providing a more reliable basis for subsequent image processing.

[0076] Further, to reduce the image deviation caused by terrain changes, the embodiment of the present application can combine open source DEM (Digital Elevation Model) data for auxiliary calculation to compensate for the error caused by terrain undulation.

[0077] Specifically, in the embodiments of the present application, when the vertical baseline of the master image and the slave image is large, the terrain undulation can cause additional offset between the images, and the local offset can be about 2 meters per 1000 meters of height change. The embodiments of the present application can correct the offset caused by the terrain based on the DEM model by using the NASADEM (NASA Digital Elevation Model) data with a resolution of 30 meters, so as to realize the accurate registration of the images. The spatial reference coordinate system of the DEM data adopts the GCS_WGS_1984 (Geographic Coordinate System_World Geodetic System_1984) standard.

[0078] In addition, after the image accurate registration is completed, the embodiments of the present application can crop the images to the range of the study area, and the range of the cropped images covers the study area. The above method can ensure the geometric accuracy and positioning accuracy of the images, so as to lay a high-quality data foundation for further analysis in the study area.

[0079] In combination with Figure 10 With a specific example, the acquisition of the ground surface deformation monitoring data by using the D-InSAR, SBAS-InSAR and DS-InSAR technologies is described in detail.

[0080] (1) The D-InSAR technology is used to monitor the ground surface deformation of the study area.

[0081] In the embodiments of the present application, the D-InSAR can extract the ground surface deformation information by performing the interference processing on the two time-phase SAR images, and the core is to calculate the interference phase of the images and eliminate the terrain phase by differential processing, so as to obtain the deformation information of the target area. In the processing process, the embodiments of the present application correct the satellite orbit error by introducing the precise orbit data, and eliminate the influence of the terrain undulation on the interference phase by using the open source DEM (Digital Elevation Model), and correct the atmospheric delay and noise, so as to improve the monitoring accuracy.

[0082] (2) The SBAS-InSAR technology is used to accurately monitor the ground surface deformation of the study area.

[0083] The embodiments of the present application filter the interferometric image pairs with small spatial baseline and time baseline, reduce the influence of atmospheric delay, orbit error and coherence reduction on the monitoring results, and thus improve the accuracy and reliability of deformation monitoring. The specific processing steps can include: performing geographic coding and coherence analysis on the preprocessed images, filtering out the interferometric image pairs meeting the conditions, and extracting the surface deformation information of the research area by using the time series analysis method. Meanwhile, the embodiments of the present application can remove and correct the noise and error in the data processing, which can ensure the accuracy of the monitoring results.

[0084] (3) The DS-InSAR technology is used to monitor the surface deformation of the research area.

[0085] The DS-InSAR technology can effectively identify and use the stable scatterers in the areas with low coherence (such as vegetation-covered areas and bare land) by analyzing the phase information of DS (Distributed Scatterers), thereby expanding the application range of the traditional PS-InSAR (Persistent Scatterer Interferometric Synthetic Aperture Radar) method. Specifically, the technology performs interferometric processing on multi-temporal SAR images, combines coherence analysis, filtering and time series modeling methods, can extract the phase stability information of the distributed scatterers, and then inverses the surface deformation of the target area. In the processing process, by combining the coherence index and the high-resolution digital elevation model (DEM), the atmospheric delay, orbit error and terrain error can be corrected, and the accuracy of deformation monitoring can be further improved.

[0086] In step S202, the coherence index of the surface deformation data is calculated.

[0087] The coherence index is a core parameter in the InSAR technology, which can be used to evaluate the coherence degree or phase stability of two radar images at a certain pixel position, and reflects whether the area can be reliably interfered and deformed.

[0088] Optionally, in an embodiment of the present application, the coherence index of the surface deformation data is calculated, including: obtaining the complex signal of the pixel point; and calculating the coherence index of the surface deformation data according to the complex signal.

[0089] Specifically, the embodiments of the present application can calculate the coherence index based on the complex signal of the pixel point, which can be used to measure the reliability of the monitoring data. The formula for calculating the coherence index can be as follows:

[0090]

[0091] wherein, γ(x, y) is the coherence value of the pixel point (x, y), and are the complex values of the primary image and the auxiliary image in the jth interferogram respectively, N is the number of the interferograms, and * represents the complex conjugate.

[0092] It should be noted that, as shown in ​ , the coherence calculation result can be used as the weight basis for monitoring the data quality, the coherence value is between 0 and 1, the higher the coherence value, the greater the contribution of the monitoring data in the fusion process; for the data of the low coherence area, a threshold can be set to remove, so as to avoid the influence of invalid or low precision data on the result.

[0093] In step S203, a bimodal Gaussian function model based on the deformation law of the coal mining subsidence area is used to generate a spatial adjustment coefficient.

[0094] Exemplarily, the surface deformation law can be manifested as that, in the time dimension, the deformation process presents a phased evolution law of “progressive-severe-stable”; or can be manifested as that, in the spatial dimension, the surface subsidence curve presents a “W” type or “U” type distribution characteristic, etc.

[0095] It should be noted that the spatial adjustment coefficient refers to a correction parameter introduced for considering the difference of different spatial positions caused by factors such as geological conditions, mining intensity, and subsidence response in the process of surface deformation, geological structure response, or data modeling. The coefficient can be set according to the sensitivity or response degree of different regions, and can be used to improve the model precision or result adaptability.

[0096] In the specific implementation of the present application, the bimodal Gaussian model can be used to accurately describe the long-term deformation law of the surface of the coal mining subsidence area. In some cases, the surface deformation law of the coal mining subsidence area generally conforms to the deformation characteristics that the main section presents a “W” type and the vertical section presents a “U” type, therefore, the bimodal Gaussian function model is adopted in the embodiments of the present application to define the deformation center and its influence range.

[0097] Optionally, in an embodiment of the present application, the calculation formula of the bimodal Gaussian function model can be expressed as:

[0098]

[0099] wherein, α i (x, y) represents the spatial correlation adjustment coefficient; λ represents the peak value for controlling the height of the bimodal; a1 and a2 both represent the main section distribution for controlling the range of the bimodal in the main section direction; b1 and b2 both represent the vertical section distribution for controlling the range of the bimodal in the vertical section direction; (x1, y1) and (x2, y2) represent the center position for controlling the position of the bimodal.

[0100] Further, in combination with ​ and ​ , and based on the actual geological characteristics and surface deformation law of the coal mining subsidence area, the step of determining the parameters of the bimodal Gaussian model is described, and the embodiments of the present application can include:

[0101] (1) Geological data analysis.

[0102] The embodiments of the present application can first obtain the mining range and time, stratum thickness, geological structure and other characteristic data of the coal mining subsidence area. Further, according to the analysis of the mining history of the coal mining subsidence area, it can be found that the 20202 working face is the last working face to stop mining, which plays a major role in long-term deformation of the surface.

[0103] (2) Historical deformation monitoring.

[0104] In the embodiments of the present application, the historical deformation law of the coal mining subsidence area is analyzed in combination with the InSAR monitoring data, and the distribution range and center position are extracted. The D-InSAR monitoring data is analyzed by the embodiments of the present application, and it can be found that the maximum surface deformation occurs near the 20202 working face, which can verify that the 20202 working face plays a major role in long-term deformation of the surface, so the surface deformation curve of the main section is extracted, and the curve conforms to the "W" type feature, so the maximum deformation position can be considered as the center position. Further, the SBAS-InSAR and DS-InSAR data are analyzed by the embodiments of the present application, the boundary position can be determined, and the distance from the boundary to the center position is calculated by using the ArcGIS9 software, that is, a i and b i are obtained.

[0105] (3) Parameter inversion.

[0106] As a specific example, based on the geological data, historical deformation characteristics and GNSS monitoring data of the coal mining subsidence area, the peak parameters of the bimodal Gaussian model can be inverted by the embodiments of the present application. Specifically, the embodiments of the present application mainly optimize the λ i of the model by minimizing the sum of squares of errors between the GNSS monitoring data and the fused settlement value, and the objective function of the optimization can be defined as:

[0107]

[0108] Wherein, N is the number of GNSS monitoring points; D GNSS (x i ,y i ) is the actual settlement value of the i-th GNSS monitoring point; D fusion (x i ,y i ) is the settlement value of the i-th point predicted by the model.

[0109] To achieve the above optimization goal, the fminunc function of MATLAB can be used to optimize the parameters in the embodiments of the present application. The function can obtain the optimal parameters λ by quasi-newton iterative search D and λ SBAS , so that the sum of squared errors is minimized. In the embodiments of the present application, the spatial adjustment coefficients of the bimodal Gaussian model finally determined are ​ the adjustment coefficients determined based on D-InSAR data in the first embodiment of the present application, and ​ the adjustment coefficients calculated based on SBAS-InSAR and DS-InSAR data in the second embodiment of the present application.

[0110] The embodiments of the present application can determine the parameters of the bimodal Gaussian model by inversion based on historical deformation data and geological conditions in the coal mining subsidence area, thereby improving the adaptability of the model to the actual deformation law.

[0111] In step S204, the adaptive weighting method is constructed based on the coherence index of the surface deformation data and the spatial adjustment coefficient to calculate the weight of the multi-source InSAR data.

[0112] It should be noted that the weight refers to a coefficient used to represent the influence degree of each data on the final result in multi-data fusion. The weight distribution strategy can dynamically adjust the contribution of each data source in different regions, so as to ensure that high-quality data occupies a dominant position in the fusion. In some cases, for the region with strong deformation, the weight of D-InSAR data can be increased; for the region with weak deformation but good stability, the weights of SBAS-InSAR and DS-InSAR data can be increased.

[0113] In the embodiments of the present application, the adaptive weighting method can be used to dynamically distribute the weights of the three kinds of InSAR data according to the geological characteristics of different regions and the coherence quality of the InSAR data, thereby improving the accuracy and reliability of the surface deformation monitoring result.

[0114] Optionally, in an embodiment of the present application, the calculation formula of the weight is:

[0115] ω i (x,y)=γ i (x,y)·α i (x,y),

[0116] wherein ω i (x,y) represents the weight; γ i (x,y) represents the coherence of the data source at (x,y); α i (x,y) represents the spatial correlation adjustment coefficient; x and y represent the coordinates of any pixel point of the InSAR data; and subscript i represents different InSAR data.

[0117] It is understandable that γ i (x, y) can reflect the data quality of a pixel; the higher the coherence value, the more reliable the data. This application's embodiments combine the coherence index γ for each type of InSAR data. i (x,y) and the adjustment coefficient α generated by the bimodal Gaussian model i The weights of each InSAR data type (x, y) can be calculated using an adaptive weighting method.

[0118] In step S205, the surface deformation monitoring data are weighted and fused using the weights of the multi-source InSAR data to obtain the final surface deformation result.

[0119] The embodiments of this application can calculate the final surface deformation result by weighting and fusing the monitoring results of different InSAR data with their corresponding weights.

[0120] Optionally, in one embodiment of this application, the fusion formula for surface deformation monitoring data is:

[0121]

[0122] Among them, D Fusion Indicates the result after fusion; ω i (x,y) represents the weight of the i-th type of InSAR data at pixel (x,y); D t (x,y) represents the surface deformation monitoring results of different InSAR data at spatial location (x,y); n represents the number of InSAR methods involved in the fusion.

[0123] In practice, the fusion process can be divided into different areas to fully consider the spatial heterogeneity of deformation characteristics in coal mining subsidence areas.

[0124] like ​ As shown in the embodiments of this application, the results obtained after fusion are improved in terms of the accuracy of surface deformation identification, spatial coverage integrity and boundary continuity compared with the results of single InSAR technology. The results after fusion can more accurately reflect the surface deformation characteristics and can significantly enhance the reliability and adaptability of surface deformation monitoring.

[0125] Optionally, in one embodiment of this application, the method further includes: verifying the surface deformation results to obtain the analysis error; and using the analysis error to optimize the model parameters of the bimodal Gaussian function model and / or the adaptive weighting method.

[0126] Combination ​, the following will be described in detail with a specific example. The embodiments of the present application can include the following steps:

[0127] (1) The fusion result is verified by GNSS monitoring data. The three-dimensional deformation data of GNSS is projected to the LOS (Line of Sight) direction.

[0128] The projection formula of the embodiments of the present application can be as follows:

[0129] D LOs = D U ·cosθ-D E ·sinθcosα+D N ·sinθsinα,

[0130] wherein, D LOS represents the deformation along the radar line of sight direction; D U , D E , D N respectively represent the eastward, northward and vertical displacement provided by GNSS data; θ represents the satellite incidence angle; and α represents the satellite flight azimuth.

[0131] (2) Linear interpolation is performed on the GNSS data to generate data matched in time with the InSAR data.

[0132] In the embodiments of the present application, the interpolation relationship can be as follows:

[0133]

[0134] wherein, represents the GNSS data at the same time as the InSAR data; and respectively represent the GNSS data containing the next scene and the previous scene within the time of the InSAR data; T b and T a represent the corresponding time.

[0135] (3) The RMSE (Root Mean Square Error) between the GNSS monitoring data and the fusion result is calculated to evaluate the precision of the fusion result.

[0136] The embodiments of the present application can calculate the RMSE by comparing the GNSS deformation data after projection and interpolation with the InSAR fusion result point by point. The formula can be expressed as:

[0137]

[0138] wherein, N represents the number of monitoring points participating in the calculation; DLOS,GNSS,i GNSS deformation value (LOS direction) of the i th monitoring point; D LOS,InSAR,i InSAR fusion deformation value (LOS direction) of the i th monitoring point.

[0139] (4) According to the verification result, the parameters of the bimodal Gaussian model and the weight distribution strategy are optimized to further improve the accuracy and stability of the fusion result.

[0140] As ​ shown, the application embodiment provides the RMSE comparison results of GNSS data and three kinds of InSAR technology and fusion data. Based on the GNSS verification result, the error distribution in the monitoring result can be analyzed, and the bimodal Gaussian model parameters and the weight distribution strategy are dynamically adjusted, so as to further improve the monitoring accuracy.

[0141] According to the coal mining subsidence area surface long-term deformation MT-InSAR adaptive fusion measurement method proposed in the embodiment of the application, the D-InSAR, SBAS-InSAR and DS-InSAR three kinds of InSAR measurement data are coupled, the coherence index is calculated, and the bimodal Gaussian function model is constructed, and then the adaptive fusion strategy is used for dynamic weighted integration of multi-source InSAR data, the monitoring of surface deformation is realized, the problems of data failure or low deformation estimation in low coherence area, large deformation area and complex surface environment of existing single InSAR technology can be made up, the accuracy, reliability and adaptability of the surface deformation monitoring in the coal mining subsidence area can be improved, and a high-efficiency and practical technical means for surface deformation monitoring in complex geological environment is provided.

[0142] Secondly, the coal mining subsidence area surface long-term deformation MT-InSAR adaptive fusion measurement device according to the embodiment of the application is described with reference to the accompanying drawings.

[0143] ​ is the block schematic diagram of the coal mining subsidence area surface long-term deformation MT-InSAR adaptive fusion measurement device according to the embodiment of the application.

[0144] As ​ shown, the coal mining subsidence area surface long-term deformation MT-InSAR adaptive fusion measurement device 10 comprises an acquisition module 100, a calculation module 200, a generation module 300, a construction module 400 and a measurement module 500.

[0145] The acquisition module 100 is configured to acquire surface deformation monitoring data in multiple ways respectively, and perform at least one of time-space alignment, noise removal and error correction on the surface deformation monitoring data to obtain surface deformation data.

[0146] The calculation module 200 is configured to calculate the coherence index of the surface deformation data.

[0147] The generating module 300 is configured to generate a spatial adjustment coefficient by using a bimodal Gaussian function model constructed based on a deformation law of a coal mining subsidence area.

[0148] The constructing module 400 is configured to construct an adaptive weighting method to calculate a weight of the multi-source InSAR data based on the coherence index of the ground surface deformation data and the spatial adjustment coefficient.

[0149] The measuring module 500 is configured to perform weighted fusion on the ground surface deformation monitoring data by using the weight of the multi-source InSAR data to obtain a final ground surface deformation result.

[0150] Optionally, in an embodiment of the present application, the method further comprises a verifying module and an optimizing module.

[0151] The verifying module is configured to verify the ground surface deformation result to obtain an analysis error.

[0152] The optimizing module is configured to optimize a model parameter of the bimodal Gaussian function model and / or the adaptive weighting method by using the analysis error.

[0153] Optionally, in an embodiment of the present application, the acquisition mode comprises multiple modes in a D-InSAR mode, an SBAS-InSAR mode and a DS-InSAR mode.

[0154] Optionally, in an embodiment of the present application, a calculation formula of the weight is as follows:

[0155] ω i (x,y)=γ i (x,y)·α i (x,y),

[0156] wherein, ω i (x,y) represents the weight of the i-th InSAR data at a pixel point (x, y); γ i (x,y) represents the coherence of the data source at (x, y); α i (x,y) represents the spatial correlation adjustment coefficient; x and y represent the coordinates of any pixel point of the InSAR data; and subscript i represents different InSAR data.

[0157] Optionally, in an embodiment of the present application, the calculating module 200 comprises an acquisition unit and a calculation unit.

[0158] The acquisition unit is configured to acquire a complex signal of a pixel point.

[0159] The calculation unit is configured to calculate the coherence index of the ground surface deformation data according to the complex signal.

[0160] Optionally, in an embodiment of the present application, the calculation formula of the double-peak Gaussian function model is:

[0161]

[0162] wherein, α i (x, y) represents the spatial correlation adjustment coefficient; λ represents the peak value; a1 and a2 both represent the main section distribution; b1 and b2 both represent the vertical section distribution, and (x1, y1) and (x2, y2) represent the center position.

[0163] Optionally, in an embodiment of the present application, the fusion formula of the surface deformation monitoring data is:

[0164]

[0165] wherein, D Fusion represents the fusion result; ω i (x, y) represents the weight of the i-th InSAR data at the pixel point (x, y); D i (x, y) represents the surface deformation monitoring result of different InSAR data at the spatial position (x, y); and n represents the number of InSAR methods participating in the fusion.

[0166] It should be noted that the aforementioned explanation and description of the embodiment of the MT-InSAR adaptive fusion measurement method for long-term deformation of the surface of the coal mining subsidence area also applies to the embodiment of the MT-InSAR adaptive fusion measurement device for long-term deformation of the surface of the coal mining subsidence area, which will not be repeated here.

[0167] According to the embodiment of the present application, the MT-InSAR adaptive fusion measurement device for long-term deformation of the surface of the coal mining subsidence area is provided, which couples three kinds of InSAR measurement data of D-InSAR, SBAS-InSAR and DS-InSAR, calculates the coherence index, constructs a double-peak Gaussian function model, and then adopts an adaptive fusion strategy to dynamically weight and integrate multi-source InSAR data, so as to realize the monitoring of the surface deformation, which can make up for the problem that the existing single InSAR technology is prone to data failure or underestimation of deformation in low coherence areas, large deformation areas and complex surface environments, and can improve the precision, reliability and adaptability of the surface deformation monitoring of the coal mining subsidence area, and at the same time provides an efficient and practical technical means for the surface deformation monitoring in complex geological environments.

[0168] ​ The structure schematic diagram of the electronic device provided by the embodiment of the present application is shown. The electronic device can include:

[0169] The memory 1001, the processor 1002, and the computer program stored in the memory 1001 and executable on the processor 1002.

[0170] The processor 1002 implements the adaptive fusion measurement method of long-term deformation MT-InSAR of a coal mining subsidence area surface provided in the above embodiments when executing a program.

[0171] Further, the electronic device further includes:

[0172] The communication interface 1003 is configured to communicate between the memory 1001 and the processor 1002.

[0173] The memory 1001 is configured to store a computer program executable on the processor 1002.

[0174] The memory 1001 can include a high-speed RAM memory, and can also include a non-volatile memory, for example, at least one disk memory.

[0175] If the memory 1001, the processor 1002 and the communication interface 1003 are independently implemented, the communication interface 1003, the memory 1001 and the processor 1002 can be connected to each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, ​ In the figure, only one thick line is used to represent the bus, but it does not mean that there is only one bus or only one type of bus.

[0176] Optionally, in a specific implementation, if the memory 1001, the processor 1002 and the communication interface 1003 are integrated on a chip, the memory 1001, the processor 1002 and the communication interface 1003 can complete communication between each other through an internal interface.

[0177] The processor 1002 can be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0178] The embodiment also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the long-term deformation MT-InSAR adaptive fusion measurement method of a coal mining subsidence area ground surface as above.

[0179] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or N embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0180] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "N" is at least two, for example, two, three, etc., unless otherwise specifically limited.

[0181] Any process or method descriptions in flow charts or otherwise described herein represent embodiments that can be understood as a set of steps, operations, or stages of implementing the custom logic function or process, and that the scope of preferred embodiments of the present application includes additional implementation involving fewer than all described steps, operations, or stages, including the performance of steps, operations, or stages in a different order, including the performance of a step, operation, or stage at substantially the same time as other steps, operations, or stages, or including the elimination of steps, operations, or stages, depending on the circumstances, as would be understood by skilled persons in the art.

[0182] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a sequence of executable instructions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor- containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be a product of the manufacturing and / or processing. The computer-readable medium can include, but is not limited to, the following: an electronic connection (an electronic device with one or N wires), a portable computer diskette (a magnetic device), a RAM (random access memory), a ROM (read-only memory), an EPROM (erasable programmable ROM) or a Flash memory, an optical fiber, and a portable CD ROM. In addition, the computer-readable medium can even be paper or other suitable medium upon which the program can be printed, because the program can be electronically captured, via the optically scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in an electronic manner into a computer storage medium, and then stored in the computer storage medium.

[0183] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware and in another embodiment, the hardware can be implemented using any or a combination of the following technologies, which are all well known in the art: a discrete logic circuit having logic gates for implementing logic functions on data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0184] Those of skill in the art would understand that the steps carried out in the above-mentioned embodiments can be implemented by programs instructing relevant hardware to complete all or part of the steps, and the programs can be stored in a computer-readable storage medium. When the programs are executed, the programs include one of the steps of the method embodiments or a combination thereof.

[0185] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each of the units can be physically present separately, or two or more units can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software functional module. When the integrated module is realized in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0186] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A long-term deformation MT-InSAR adaptive fusion measurement method for a coal mining subsidence area surface, characterized in that, The method comprises the following steps: obtaining ground deformation monitoring data by using multiple methods respectively, and performing at least one of time-space alignment, noise removal and error correction on the ground deformation monitoring data to obtain ground deformation data; calculating a coherence index of the ground deformation data; generating a spatial adjustment coefficient by using a bimodal Gaussian function model constructed based on the deformation law of a coal mining subsidence area; constructing an adaptive weighting method based on the coherence index of the ground deformation data and the spatial adjustment coefficient to calculate the weight of multi-source InSAR data; performing weighted fusion on the ground deformation monitoring data by using the weight of the multi-source InSAR data to obtain a final ground deformation result.

2. The method of claim 1, wherein, It also comprises: verifying the ground deformation result to obtain an analysis error; optimizing the model parameters of the bimodal Gaussian function model and / or the adaptive weighting method by using the analysis error.

3. The method of claim 1, wherein, The multiple methods include multiple methods selected from the group consisting of D-InSAR, SBAS-InSAR and DS-InSAR.

4. The method of claim 3, wherein, The calculation formula of the weight is: ω i (x,y) = γ i (x,y) · α i (x,y), where ω i (x,y) represents the weight of the i-th InSAR data at the pixel point (x,y); γ i (x,y) represents the coherence of the data source at (x,y); α i (x,y) represents the adjustment coefficient of spatial correlation; x and y represent the coordinates of any pixel point of the InSAR data; subscript i represents different InSAR data.

5. The method of claim 1, wherein, The calculation of the coherence index of the ground deformation data comprises: obtaining a complex signal of a pixel point; calculating the coherence index of the ground deformation data according to the complex signal.

6. The method of claim 1, wherein, The calculation formula of the bimodal Gaussian function model is: wherein α i (x,y) represents the spatial correlation adjustment coefficient; λ represents the peak value; a1, a2 both represent the main section distribution; b1, b2 both represent the vertical section distribution, (x1, y1) and (x2, y2) represent the center position.

7. The method of claim 1, wherein, The fusion formula of the ground deformation monitoring data is: wherein D Fusion represents the result after fusion; ω i (x, t) represents the weight of the i-th InSAR data at the pixel point (x, y); D i (x, y) represents the surface deformation monitoring result of different InSAR data at the spatial position (x, t); and n represents the number of InSAR methods participating in the fusion.

8. A long-term deformation MT-InSAR adaptive fusion measuring device for a coal mining subsidence area surface, characterized in that, It comprises: an acquisition module, configured to obtain ground deformation monitoring data by using multiple methods respectively, and perform at least one of time-space alignment, noise removal and error correction on the ground deformation monitoring data to obtain ground deformation data; a calculation module, configured to calculate a coherence index of the ground deformation data; a generation module, configured to generate a spatial adjustment coefficient by using a bimodal Gaussian function model constructed based on the deformation law of a coal mining subsidence area; a construction module, configured to construct an adaptive weighting method based on the coherence index of the ground deformation data and the spatial adjustment coefficient to calculate the weight of multi-source InSAR data; a measurement module, configured to perform weighted fusion on the ground deformation monitoring data by using the weight of the multi-source InSAR data to obtain a final ground deformation result.

9. An electronic device, comprising: It comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the coal mining subsidence area long-term deformation MT-InSAR adaptive fusion measurement method according to any one of claims 1-7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the coal mining subsidence area long-term deformation MT-InSAR adaptive fusion measurement method according to any one of claims 1-7.

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